Abstract 1184: Modeling mechanisms of resistance of epidermal growth factor receptor (EGFR) mutations to targeted drugs through patient-derived xenografts (PDX) of non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
Abstract Background: Resistance to small molecule EGFR tyrosine kinase inhibitors (TKIs) is seen in some NSCLC patients with activating EGFR mutations. We evaluated in vivo PDX models for their utility in studying EGFR-targeted drug resistance mechanisms. Methods: Surgically resected early stage NSCLC tumors were implanted into non-obese diabetic severe combined immune deficient (NOD-SCID) mice. Tumors were passaged and expanded in new mouse hosts once the humane endpoint of 1.5 cm maximum diameter was reached. EGFR TKI treatment was initiated at an average tumor volume of 150mm3. Treatments included daily oral gavage with first and second generation EGFR TKIs, and with weekly intraperitoneally administered cetuximab. Results: Of the 55 NSCLC tumors with EGFR activating mutations, only 6 engrafted (11%) and could be propagated beyond the first passage, and 4 have been studied for their responsiveness to EGFR-targeted agents. Model 148, developed from a patient who received pre-operative erlotinib, showed intrinsic pan-resistance to all EGFR-targeted therapies despite having an L858R mutation. The corresponding patient did not respond to erlotinib, relapsed after surgery and did not receive additional TKI therapy. Model 137, with an exon19 E746-A750 deletion, recapitulated the patient's response to gefitinib at relapse; this model was sensitive to first and second generation EGFR TKIs. Model 192 also has the exon19 E746-A750 deletion, however it did not recapitulate the patient's observed sensitivity to erlotinib. Selection for a MET amplified population during engraftment may be the cause for the disparate drug sensitivities. Model 164 has a double exon19 L747-T751 deletion and T790M EGFR mutation. Neither patient nor xenograft responded to erlotinib; the xenograft responded to cetuximab. Resistance developed over time to a second generation EGFR TKI; this resistant phenotype was not stable as each subsequent passage of the ‘resistant’ tumor exhibited the same initial response pattern. Two potential mechanisms for this transient sensitivity are currently being investigated: epigenetic mechanisms and intratumoural mutational heterogeneity. Conclusions: PDX models may provide important insight into biomarkers and mechanisms of resistance to targeted therapies, and provide a means to test novel treatment strategies to improve future treatment efficacies. Citation Format: Erin L. Stewart, Celine Mascaux, Shingo Shakashita, Devang Panchal, Dennis Wang, Ming Li, Nhu-An Pham, Natasha Leighl, Geoffrey Liu, Frances A. Shepherd, Ming-Sound Tsao. Modeling mechanisms of resistance of epidermal growth factor receptor (EGFR) mutations to targeted drugs through patient-derived xenografts (PDX) of non-small cell lung cancer (NSCLC). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1184. doi:10.1158/1538-7445.AM2014-1184
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".